Neural Network-Based Design of Approximate Gottesman-Kitaev-Preskill Code

TL;DR

This paper uses neural networks to design optimized approximate GKP quantum error-correcting codes that require fewer resources and outperform traditional codes in error correction capabilities.

Contribution

It introduces a neural network approach to generate approximate GKP states that are more resource-efficient and have better error correction performance than conventional methods.

Findings

Optimized GKP codes outperform traditional codes with fewer squeezed states.

Neural network-designed codes require only one-third of the squeezed states at 9.55 dB squeezing.

The approach simplifies codewords while enhancing error correction effectiveness.

Abstract

Gottesman-Kitaev-Preskill (GKP) encoding holds promise for continuous-variable fault-tolerant quantum computing. While an ideal GKP encoding is abstract and impractical due to its nonphysical nature, approximate versions provide viable alternatives. Conventional approximate GKP codewords are superpositions of multiple {large-amplitude} squeezed coherent states. This feature ensures correctability against single-photon loss and dephasing {at short times}, but also increases the difficulty of preparing the codewords. To minimize this trade-off, we utilize a neural network to generate optimal approximate GKP states, allowing effective error correction with just a few squeezed coherent states. We find that such optimized GKP codes outperform the best conventional ones, requiring fewer squeezed coherent states, while maintaining simple and generalized stabilizer operators. Specifically, the former outperform the latter with just \textit{one third} of the number of squeezed coherent states at a squeezing level of 9.55 dB. This optimization drastically decreases the complexity of codewords while improving error correctability.